Non-contrast Enhanced MRI in Central Nervous System

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• Professor Meiyun Wang
• Friday 22 March 2019, 14:00-15:00
• MR11, Centre for Mathematical Sciences, Wilberforce Road, Cambridge.

If you have a question about this talk, please contact J.W.Stevens.

In this talk, the applications of three non-contrast enhanced advanced MR techniques, including Diffusion Kurtosis Imaging (DKI), Strategically Acquired Gradient Echo (STAGE) and a Length and Offset Varied Saturation (LOVARS) in central nervous system (CNS) will be summarized. DKI has been used to measure non-Gaussian diffusion, which has the potential to characterize both normal and pathologic tissue better than diffusion-tensor imaging. Some previous researchers have suggested that DKI might provide more accurate information about water diffusion. Our study showed that mean kurtosis values may provide additional information and improve the grading of gliomas compared with conventional diffusion parameters. STAGE is an advanced Susceptibility weighted imaging (SWI) which can provide multi-contrast images in one scan, such as T1W , PDW, T1 MAP , PD MAP , R2* MAP , SWI, and even MRA images in 5 minutes. It’s very helpful in early detecting and evaluating ischemia, brain trauma and some other CNS diseases. LOVARS is a novel Chemical exchange-dependent saturation transfer (CEST) technique with variable length of saturation for 1 pair of offsets, which can detect endogenous macromolecules (e.g. Tumor associated glycoprotein MUC -1) and metabolites. Some preliminary studies have showed that LOVARS is not only helpful in early detection and separation of cerebral ischemia and intracranial hemorrhage, but also in grading glioma, evaluating the therapy effect and even differentiating treatment effect from tumor reoccurrence. Furthermore, it can also detect and differentiate cerebral ischemia from intracranial hemorrhage at very early stage. So it may have the potential to improve the diagnostic flow for early stroke. But its post-processing is still complicated.

This talk is part of the CMIH Hub seminar series series.

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